Add `--dataloader_num_workers` to the DDPM training example (#1027)

examples/unconditional_image_generation/train_unconditional.py

@@ -83,7 +83,16 @@ def parse_args():
         "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
     )
     parser.add_argument(
-        "--eval_batch_size", type=int, default=16, help="Batch size (per device) for the eval dataloader."
+        "--eval_batch_size", type=int, default=16, help="The number of images to generate for evaluation."
+    )
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
+            " process."
+        ),
     )
     parser.add_argument("--num_epochs", type=int, default=100)
     parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
@@ -249,7 +258,9 @@ def main(args):
         return {"input": images}
 
     dataset.set_transform(transforms)
-    train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True)
+    train_dataloader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
+    )
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,